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Reviews and syntheses : Remotely sensed optical time series for monitoring vegetation productivity

Kooistra, Lammert (författare)
Wageningen University
Berger, Katja (författare)
University of Valencia
Brede, Benjamin (författare)
GFZ German Research Centre for Geosciences
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Graf, Lukas Valentin (författare)
ETH Zürich,Agroscope
Aasen, Helge (författare)
ETH Zürich,Agroscope
Roujean, Jean Louis (författare)
National Centre for Space Studies (CNES)
Machwitz, Miriam (författare)
Luxembourg Institute of Science and Technology (LIST)
Schlerf, Martin (författare)
Luxembourg Institute of Science and Technology (LIST)
Atzberger, Clement (författare)
Prikaziuk, Egor (författare)
Faculty of Geo-Information Science and Earth Observation – ITC
Ganeva, Dessislava (författare)
Space Research and Technology Institute
Tomelleri, Enrico (författare)
Free University of Bozen-Bolzano
Croft, Holly (författare)
University of Sheffield
Reyes Muñoz, Pablo (författare)
University of Valencia
Garcia Millan, Virginia (författare)
University of Malaga
Darvishzadeh, Roshanak (författare)
Faculty of Geo-Information Science and Earth Observation – ITC
Koren, Gerbrand (författare)
Copernicus Institute of Sustainable Development
Herrmann, Ittai (författare)
Hebrew University of Jerusalem
Rozenstein, Offer (författare)
Agricultural Research Organization, Volcani Center
Belda, Santiago (författare)
University of Alicante
Rautiainen, Miina (författare)
Aalto University
Rune Karlsen, Stein (författare)
NORCE Norwegian Research Centre
Figueira Silva, Cláudio (författare)
Cerasoli, Sofia (författare)
Pierre, Jon (författare)
Tanlr Kaylkçl, Emine (författare)
Karadeniz Technical University
Halabuk, Andrej (författare)
Slovak Academy of Sciences
Tunc Gormus, Esra (författare)
Karadeniz Technical University
Fluit, Frank (författare)
Wageningen University
Cai, Zhanzhang (författare)
Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science
Kycko, Marlena (författare)
University of Warsaw
Udelhoven, Thomas (författare)
University of Trier
Verrelst, Jochem (författare)
University of Valencia
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 (creator_code:org_t)
2024
2024
Engelska 39 s.
Ingår i: Biogeosciences. - 1726-4170. ; 21:2, s. 473-511
Relaterad länk:
http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
https://doi.org/10.5...
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  • Forskningsöversikt (refereegranskat)
Abstract Ämnesord
Stäng  
  • Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

Ämnesord

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Naturgeografi (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Physical Geography (hsv//eng)

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